The Unfairness of Multifactorial Bias in Recommendation

TL;DR

This paper investigates how combined popularity and positivity biases, termed multifactorial bias, affect fairness in recommender systems, and proposes a simple pre-processing method to mitigate their impact effectively.

Contribution

It introduces the concept of multifactorial bias in recommendation fairness and proposes a percentile-based rating transformation to reduce exposure bias with minimal accuracy loss.

Findings

Positivity bias amplifies popularity bias, increasing over-exposure of popular items.

Pre-processing rating transformation improves exposure fairness across multiple algorithms.

The method reduces computational costs while enhancing fairness in recommendation systems.

Abstract

Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfair or suboptimal outcomes. Popularity bias occurs when a small subset of items receives most interactions, while positivity bias stems from the over-representation of high rating values. Although each bias has been studied independently, their combined effect, to which we refer to as multifactorial bias, remains underexplored. In this work, we examine how multifactorial bias influences item-side fairness, focusing on exposure bias, which reflects the unequal visibility of items in recommendation outputs. Through simulation studies, we find that positivity bias is disproportionately concentrated on popular items, further amplifying their over-exposure. Motivated by this insight, we adapt a percentile-based rating transformation as a pre-processing strategy to mitigate multifactorial bias. Experiments using six recommendation algorithms across four public datasets show that this approach improves exposure fairness with negligible accuracy loss. We also demonstrate that integrating this pre-processing step into post-processing fairness pipelines enhances their effectiveness and efficiency, enabling comparable or better fairness with reduced computational cost. These findings highlight the importance of addressing multifactorial bias and demonstrate the practical value of simple, data-driven pre-processing methods for improving fairness in recommender systems.